The Italian Corporate System - European Historical Economics Society

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The Italian Corporate System - European Historical Economics Society

The Italian Corporate System:
SOEs, Private Firms and Institutions in a
Network Perspective (1952-1983)
L. Bargigli∗†, R. Giannetti*
May 28, 2015
Abstract
How did business networks among Italian firms evolve over time?
We address this question by analyzing the Italian corporate boards
network in four years (1952, 1960, 1972, 1983). We find some typical properties, such as sparsity and connectedness in the same large
network component. Some structures, such as core-periphery subdivision, are substantially stable. Clustering and assortativity are relatively high and stable, while we observe an increase of the average
distance coupled with a decrease of density and of the relative size of
the largest component: this is an indication of a rarefaction of connections. A panel regression for the average nodal degree shows that
rarefaction is mostly related to a genuine time trend and only partially
to cross-sectional variables. We argue that a possible explanation is
a significant increase of concentration. Looking at the main actors
we find a persistent centrality of banks and insurances, as well as of
State Owned Enterprises (SOEs), which play a growing role in the
community structure of the network.
Keywords: boards networks, network theory, Italian firms.
JEL Classification: N00, L20, C49
∗
Dipartimento di Scienze per l’Economia e l’Impresa, Università di Firenze, via delle
Pandette 9, 50127 Firenze (Italy).
Contacts: [email protected]; [email protected]
†
Corresponding author.
1
1
Introduction
The analysis of networks of business firms is a relatively recent and promising
perspective in the study of business organization. One of the most interesting topics concerns the evolution of business networks, which factors are
responsible for their characteristics at some point in time, as well as for their
variations over time. In particular, this paper focuses on the connections
between the boards of Italian business firms in the “long” Golden Age, using
a comprehensive source with data on more than 38,000 companies in four
benchmark years (1952, 1960, 1972, 1983).
We aim to extend the existing historical literature on this topic by adopting a set of methods of complex networks theory. These methods, which are
mostly derived from statistical physics (Park and Newman, 2004; Squartini
and Garlaschelli, 2011), allow to perform rigorously statistical inference over
network properties, something which is new to the historical analysis on this
subject up to date. Moreover, we are able to apply this approach also to the
detection of communities, i.e. of subsets of strongly connected firms (Fortunato, 2010). By using this methodology, together with other more common
analyses such as core-periphery subdivision and node centrality, we are able
to revise some of the conclusions of previous works, e.g. by showing that
State Owned Enterprises (SOEs) and banks maintain an important position
in the system throughout the period. On the other hand, we are able to
compare the Italian network with other national networks as described by
the literature, showing that it is no different from the latter when looking at
the most relevant network properties.
From an historical perspective, we integrate statistical network analysis
with two more appreciative priors. The first one concerns the general consideration of the Golden Age as the age of Big Business, while the second one
concerns the institutional environment of networks, especially the characters
of a very peculiar financial system as the Italian one was. Both priors could
potentially affect the structure of the network and its evolution over time. In
particular, the most relevant trend we observe is a decrease of connections
which is common to other national systems in the same period, although the
core of the network maintains a surprising stability over the time, particularly with respect to the centrality of its main actors, banks and insurances.
We circumscribe the role of cross-sectional variables, such as size or sectoral
composition, then we relate the rarefaction between 1972 and 1983 to an
increasing concentration of the system in terms of assets, which is an out2
come of a wave of mergers due to increasing indebtedness of non financial
companies with the banking system.
The rest of the paper is organized as follows. In Sec. 2 we review the
main historical contributions on the topic of the Italian corporate system. In
Sec. 3 we shortly describe the main source for our analysis. In Sec. 4, we
recall the main results of the literature on boards networks and analyze the
topological properties of the Italian system. In Sec. 5 we perform the coreperiphery partition of the network. In Sec. 6 we look at the most central firms
by betweenness. In Sec. 7 we perform the community decomposition of the
network, and detect the statistically significant characters of each community.
In Sec. 8 we test a model for node connectivity aimed at explaining the
observed decrease of this quantity over time. Finally, Sec. 9 concludes. The
technical apparatus of the analysis is detailed in three appendices.
2
Historical literature
In the Italian economic history the topic of the corporate system has for the
most part been tackled by focusing on collusion, since a few large, monopolistic firms dominate the domestic market. In a macroeconomic perspective
of backwardness and catching up, few economic historians (Zamagni, 1993;
Bonelli, 1979) gave a relatively positive judgment of this configuration. According to their view, the few large, connected firms and a strong State
intervention allowed to adopt the leading technologies (iron and steel, electricity, chemicals, etc.) required to catch up modern economic growth, while
the financial system, regulated by the State, canalized resources to exploit
these technologies. From a similar perspective, De Cecco (2001) delimited
the efficiency of this collusive capitalism up to the virtuous Fifties and the
early Sixties of the 20th century. Afterwards negative consequences prevailed,
starting in the midst of the Sixties and lasting up to the crisis of 1992, when
oligopolistic collusion started to decrease slowly with liberalizations and privatizations.
On the contrary, most Italian scholars underlined that this configuration
was an obstacle for economic growth. Sereni (1966) emphasized the limitation of market size, Sylos Labini (1970) the obstacle it posed to innovation. A
majority of scholars emphasized the role of the specific institutional arrangement of ownership (“salotti buoni”) and of organization (business groups)
entangled in a network of relationships between the banks, the State and
3
the large private and public firms (Rossi and Toniolo, 1992; Barca, 1996;
Amatori, 1995). More recently Aganin and Volpin (2005) argued that Italy
is still blocked in a State and family capitalism in which a restricted élite
of politically appointed bureaucrats and of wealthy families take a central
position.
The empirical evidence to support the one or the other position is, for
the most part, thin, although more consistent in the period after World War
II. Immediately after the war, the Economic Commission of the Ministry
for the Constituent Assembly made a very detailed survey of Italian jointstock companies (Ministero per la Costituente, 1947). The results of the
survey highlighted that a few large corporate groups dominated the Italian
economy by controlling, directly or indirectly, three-quarters of the total
equity of private firms. In the early 1960s, Benedetti and Toniolli (1963)
found that electricity companies managed a dense network of connections
among themselves as well as with other industrial sectors. This configuration
changed drastically after the nationalization of the electrical industry (1962).
Ragozzino (1969) noted that this event represented a systemic change of the
industrial and financial relations centered on the big electrical firms and
their close relationship with banks and insurances. The consequence was the
emergence of a new order in which State firms and family groups, like Fiat
and Pirelli, took the center of the system up to the age of privatizations in
the Nineties.
In the Eighties, Chiesi (1982, 1985) introduced the more advanced tools
of network analysis of boards to study the configuration of Italian capitalism. He identified the peculiarities of the Italian corporate network in the
economic intervention of the State. Chiesi identified, in the mid Seventies,
two poles represented by SOEs and private enterprises. He also identified a
significant integration between the two sets, guaranteed by companies such
as Sme, Bastogi, and, to a lesser extent, Snia-Viscosa. Chiesi also emphasized the key position of SOEs and the absence of the two most important
private groups, Fiat and Pirelli, from the center of the network. Ferri and
Trento (1997) using the same tools but a reduced sample of companies, found
instead dense relations between private companies and SOEs at least until
1970. Rinaldi and Vasta (2005) explored with the same tools the structure of
the Italian corporate system during the 1952-72 period using the same sample we use here. They identified dense connections among the major private
firms, a significant connection with SOEs and changing leading clusters of
firms. In 1952 and 1960, electrical companies showed the highest cohesion; af4
ter the nationalization, they identified a new and less cohesive network made
by financial intermediaries: banks, insurances and major finance companies.
In a more recent work, Rinaldi and Vasta (2012) extended their analysis
to 1983, finding that the cohesion of the system had sharply declined and
that SOEs were no more at its center, while a new private merchant bank,
Mediobanca, emerged as key actor.
Bianco and Pagnoni (1997) analyzed the ties among board members of
the Italian listed companies from 1985 to 1995. They showed that the sharing of board members was a common practice between companies in the
sample. In general, these connections signaled the position of the controlling
subject placed at the head of pyramidal groups. Conversely, in the presence
of a legislation limiting banks participations in non financial companies (and
vice versa), boards interconnections between banks and industrial companies
served as substitutes of cross shareholdings. A further work by Bianco et
al. (2009) extended the analysis from 1998 to 2008. The main results were
that over the entire period a high share of the companies in the sample were
connected with each other. The company network was centered around the
financial and non-financial Blue Chips. The directors who ensured the bulk
of connectivity by serving in a higher number of boards were mainly relevant
shareholders or managers of the Blue Chips. The turnover of the main board
interlockers tended to follow their turnover as shareholders in the same companies. Corrado and Zollo (2006) investigate the role of corporate governance
reforms and privatization programs of the Nineties in shaping the structural
characteristics of network among Italian firms, by looking at cross shareholdings. They find a significant fragmentation of the overall network, but at the
same time a stability in the structure of its main component, measured by
small-world coefficients, i.e. the key players in the network seem to remain
relatively stable despite the major turbulence at the institutional level.
3
Dataset
The dataset of this paper is the digitalization of the serial source “Notizie
statistiche sulle principali società per azioni” published between 1908 and
1926 by Credito Italiano1 and afterwards by the Associazione fra le società
1
In particular, in the following years: 1908, 1910, 1912, 1914, 1916, 1918, 1920, 1922,
1925.
5
italiane per azioni, up to 19842 . The source contains information regarding
companies, boards of directors and balance sheets of a large sample of Italian joint-stock companies for several benchmark years. It includes all the
joint-stock companies listed on the Milan stock exchange, and companies
located in Italy whose equity at the closure of the last balance was higher
than a given threshold, which varied from year to year. On the whole, the
dataset contains data on more than 38,000 companies, almost 300,000 directors, and more than 100,000 balance sheets. Its representativeness, in terms
of equity, is very high as the sample covers well over 90% of the total in all
but the first two benchmark years (1911 and 1913) and the last one (1983),
for which the proportion is around 85%. The digital version of the source
(http://imitadb.unisi.it/) includes the data contained in the printed volumes
and it is composed of three archives (Vasta, 2006):
1. a company dataset for the benchmark years (1911, 1913, 1921, 1927,
1936, 1952, 1960, 1972, 1983) covering the following items: firm name;
year of foundation; head office; share capital and paid-up capital;
2. the members of board of directors and board of auditors for the same
years;
3. balance sheet data covering all the years between 1960 and 1971 for the
following items: share capital; reserves; physical assets; inventory; securities and investments; cash and credits; bonds; debts; sinking founds;
reserves; profits (losses); total and per share dividends.
This paper uses the company data set and the list of board of directors
for the years 1952, 1960, 1972, 1983. For each year the network of boards is
obtained as described in Appendix A.
4
Network properties
The literature on interlocking directorates which employs network theoretical
methods has detected a number of recurrent properties of boards networks3 .
These are low average distances coupled with relatively high values of the
2
In particular in the following years: 1928, 1930, 1932, 1934, 1937, 1940, 1949, 1953,
1956, 1958, 1961, 1964, 1967, 1970, 1973, 1980, 1984.
3
For definitions and notation see Appendix A.
6
clustering coefficient, which are a signature of the so-called “small world
model” (Watts and Strogatz, 1998). Regarding the former measure, typical
values range, depending on network size, between 3 and 6, while the latter
ranges from 0.20 for the US, Swiss networks (Davis et al., 2003; Daolio et
al., 2011) to 0.57 for the German network (Conyon and Muldoon, 2006).
A third recurrent characteristics is assortativity, according to which nodes
with a similar degree value are more likely to be neighbors. This feature,
which is ubiquitous in social networks, has been detected in different national
(Battiston and Catanzaro, 2004) and international datasets (Heemskerk and
Fennema, 2009; Burris et al., 2012).
Board networks are typically very sparse, which means that only a small
fraction of the potential connection are put in place. This is reflected in
density values of the order of up to 5% for smaller samples (Battiston and
Catanzaro, 2004) and down to 0.4% for larger samples (Conyon and Muldoon,
2006). At the same time, most nodes belong to the same, large, connected
component, the so-called “giant component”. This feature cannot be related
to some special characteristics of board networks since their average node
degree always exceeds unity, and a network formed by independent random
links would display the same behavior under this condition. On the other
hand, the fraction of nodes belonging to the giant component changes sensibly
for different networks, e.g. it is 24% for Germany and 84% for U.S. according
to Conyon and Muldoon (2006).
For a proper understanding of these stylized facts and of their variability, we should take into account the following considerations. Firstly, the
samples employed in the literature are heterogeneous, both by selection criteria and by size. In particular, most samples include a few hundred of the
largest national firms by market capitalization. These firms represent the
most connected “core” of the larger, unobserved, national network. Then
larger samples will typically include firms with lower degrees. This affects
negatively connectivity measures, like average degree and especially density,
as well as the size of the giant component. Secondly, these measures are
far from stable over time, while samples take snapshots at different years.
In particular, while the density of national networks appears to be decreasing in the last decades (Battiston and Catanzaro, 2004; Davis et al., 2003;
Bellenzier and Grassi, 2014; Heemskerk and Fennema, 2009), the density of
international networks appears instead to be increasing (Heemskerk, 2011,
2013; Burris et al., 2012).
With these premises in mind, we turn to the board networks represented
7
in our dataset. From Tab. 1 we see that the Italian boards network shares
the properties mentioned above. In particular, density is relatively low, while
clustering and assortativity relatively high with respect to other national
networks. Regarding clustering, in contrast with poorly clustered networks
(Bargigli et al., 2014), we observe a large number of highly connected nodes
with a large clustering coefficient, although the usual inverse relationship still
holds to a large extent (Fig. 1). While assortativity and clustering are stable
over time, we observe an increase of the average distance coupled with a
decrease of density and of the relative size of the giant component. This is a
clear indication of a rarefaction of connections which is consistent with other
national boards networks, as well as with previous results obtained from the
Imita dataset (Rinaldi and Vasta, 2012) . We will develop this point further
in Sec. 8.
Statistics
N
|Cg |
|Cg |/N
|E|
w
¯l
D
ρk
ρw
cc
¯
T
1952
1960
1972
1983
4,357
3,747
0.86
23,754
30,773
4.51
0.0025
0.3910
0.3978
0.5023
296,898
4,606
3,913
0.85
26,076
33,342
4.49
0.0025
0.4060
0.3887
0.5063
332,205
7,939
6,153
0.77
34,726
43,727
5.09
0.0011
0.4141
0.3756
0.5180
368,340
3,763
2,761
0.73
12,493
17,213
5.00
0.0018
0.4023
0.3902
0.4835
74,136
N = n°of boards; |Cg | = n°of boards in the giant component;
|E| = n°of links; w = total strength; l̄ = average path length;
D = network density; ρk = degree assortativity; ρw = strength
assortativity; cc
¯ = average board clustering coefficient; T = n°of
triangles.
Table 1: Network properties, full sample
In order to analyze some of the measures above, it is convenient to introduce the idea of “null network models”. In practice, following a standard statistical inference approach, the significance of the observed values
is evaluated against the null statistical hypothesis that nodes are randomly
connected. This hypothesis is tailored to the observed network through a
8
clustering_vs_degree
clustering_vs_degree
1.0
1.0
0.8
0.8
0.6
cc
cc
0.6
0.4
0.4
0.2
0.2
0.0
20
40
60
80
100
120
140
160
180
0.0
50
100
150
k
k
(a) 1952
(b) 1960
clustering_vs_degree
clustering_vs_degree
0.8
0.8
0.6
0.6
cc
1.0
cc
1.0
0.4
0.4
0.2
0.2
0.0
50
100
0.0
150
k
10
20
30
40
50
60
70
k
(c) 1972
(d) 1983
Figure 1: Degree versus clustering coefficient.
specific set of constraints. In particular we require that both the average
degree and strength of each node, in a large sample of random networks, are
equal to those of the same node in the real network (for technical details see
Appendix B).
While the model specification through either degrees or strengths is standard in the network literature where it is labeled as “configuration model”
(Newman et al., 2001; Squartini and Garlaschelli, 2011), the “generalized”
specification through both constraints is rather innovative and more powerful (Garlaschelli and Loffredo, 2009; Bargigli, 2014). On the one hand,
random binary models cannot evaluate weighted properties by construction,
9
on the other hand, the models for weighted networks, which are specified
only through the strength sequence, do not have a realistic topology, since
they are not sparse (Bargigli, 2014). Thus any topological property of real
networks becomes trivially significant against such a null model.
In order to explain why degrees and strengths are chosen as constraints,
it is useful to introduce the idea of a hierarchy of network observables. First
order properties of a network involve only linear combinations of the elements
of the adjacency or weight matrix. These properties include the degree and
strength sequences. Analogously one can define second, third, etc. order
properties (generically higher order properties) as those metrics that involves
sums of products of two, three, etc. elements of the adjacency or weight
matrix. Since the value of higher order properties is not dictated by lower
order properties, the approach is thus to specify the model through relevant
low order properties and to observe whether higher order properties of the
real network are reproduced by the model. For example, let us consider
assortativity. After having observed a value of 0.40 in 1983, we ask: which
value would we find if we allowed each board to retain the same number of
neighbors it has in reality, but to randomly choose its counterparties? More
generally, which interaction patterns in a real network are “unexpected” when
one assumes that certain properties of nodes are preserved?
From Tab. 2 we see that all the higher order properties considered turn
out to be highly significant in our networks. In other terms, our null hypothesis is always rejected, and this means that the observed values cannot
be regarded as the outcome of a purely random interaction between nodes.
In particular, we see that the average distance and the number of triangles
are significantly high, while the size of the largest component is significantly
low. These results should be read together. The high number of triangles
implies that a large fraction of links runs between nodes that would belong
to the same component even if these links would be absent, thus lowering
the probability that a random link connects nodes in two otherwise different components. Moreover, we know that the average distance in a highly
clustered network is higher than in a purely random network (Watts and
Strogatz, 1998).
Assortativity is highly significant too, in particular we see that in a random sample of networks we would expect this quantity to be slightly negative,
i.e. the networks to be disassortative. In the literature on social networks,
high clustering and assortativity have a respective standard interpretation
in terms of social interactions (since, as the argument goes, a friend of a
10
friend is likely to be a friend) and homophily (Newman and Park, 2003). In
the economic context, other factors may be at work. For instance, firms’
size influences positively their connectivity in terms of degree or strengths.
Then assortativity might reflect also the fact that larger firms form strategic
alliances between themselves for a number of economically relevant reasons.
We will develop this point further in Sec. 8.
¯l
(p-values)
(sample average)
1952
1960
1972
1983
4.515
4.489
5.092
5.000
(0.000)
(0.000)
(0.000)
(0.000)
|Cg |
(2.988)
3,747
(2.986)
3,913
(3.268)
6,153
(3.301)
2,761
(p-values)
(sample average)
(0.000)
(4,237)
(0.000)
(4,476)
(0.000)
(7,676)
(0.000)
(3,633)
ρk
(p-values)
(sample average)
ρw
0.391
0.406
0.4141
0.4023
(0.000)
(-0.0301)
(0.000)
(-0.0289)
(0.000)
(-0.0165)
(0.000)
(-0.0136)
0.3978
0.3887
0.3756
0.3902
(p-values)
(sample average)
(0.000)
(-0.0259)
T
(p-values)
(sample average)
(0.000)
(0.000)
(0.000)
296,898
(-0.0256)
332,205
(-0.0136)
368,340
(-0.009)
74,136
(0.000)
(129,073)
(0.000)
(144,817)
(0.000)
(74,889)
(0.000)
(16,570)
Table 2: Significance against the null model, full sample.
According to network theory, the degree / strength distributions are very
important to explain some network properties like resilience to the removal of
nodes. This issue is not tackled systematically in the literature on board networks. Notable exceptions are Grassi (2010), who rejects the hypothesis that
the right tail of degrees is power-law distributed, Piccardi et al. (2010) and
Heemskerk (2013), who claim that it is exponentially distributed. Caldarelli
and Catanzaro (2004) compare instead degrees with strengths, observing
that, while the behavior of the former looks exponential, the latter seem to
follow a power-law distribution. In order to assess comprehensively this issue, we follow the approach suggested by Clauset et al. (2009), namely we
compute the log-likelihood ratios of a set of alternative distributions (power11
law, power-law with exponential cutoff, exponential, lognormal) which are
consistently estimated from the data through maximum likelihood methods4 . Regarding degrees, we observe a weak evidence in favor of a power-law
with cutoff. In fact, the log-likelihood ratios are generally in favor of this
hypothesis, but significant only in 1983 against the lognormal and simple
power-law, while the exponential distribution can never be rejected. Regarding strengths, the power-law with cutoff is significantly preferred to a simple
power-law in all years but one (1972), while the lognormal and exponential
alternatives cannot be rejected.
These results are rather inconclusive, suggesting that the “true” datagenerating probability distribution for degrees, if any, could be almost impossible to detect. This difficulty is not surprising once we take into account
the finite size effects which make the rightmost part of the distributions unobservable. Furthermore, we should take into account the fact that the empirical distributions are changing with time (Fig. 2). Thus the hypothetical
“true” distribution is not even stable over time. This “lack of universality”
in distributions appears to be typical of economic networks (Bargigli et al.,
2014).
1.0
1.0
0.9
0.9
0.8
0.8
0.7
0.7
0.6
1952_vs_1960
1952_vs_1960
1952_vs_1972
1952_vs_1983
1952_vs_1972
0.6
1952_vs_1983
1960_vs_1972
1960_vs_1972
1960_vs_1983
0.5
1960_vs_1983
1972_vs_1983
0.5
0.6
0.7
0.8
0.9
1972_vs_1983
1.0
0.6
(a) degree
0.7
0.8
(b) weight
Figure 2: PP-plots
4
The results of this procedure are detailed in Tab. D.1, Appendix D.
12
0.9
1.0
5
Core-Periphery
The core-periphery subdivision is a widely investigated feature of networks.
In the baseline model, the core is a subset of nodes which are maximally
connected with other core members, while the periphery is the complementary subset of nodes with no reciprocal connections (Borgatti and Everett,
2000). Following Lip (2011) it is possible to show analytically that the coreperiphery subdivision in a symmetric network depends only on the degree
distribution of the network. In fact, once the nodes are sorted in decreasing
order of degree, the necessary and sufficient condition for the ith node to be
included in the core is that its degree ki satisfies the inequality i − 1 < ki .
From this condition we see that the core is empty only if the network is
trivially void of links.
The relationship between core-periphery structure and disassortativeness
may be explained through the following informal argument. Suppose that
core members have a degree equal to n on average, while the core size is k with
n k. Then the average core member must have at least n − k links with
peripheral nodes, i.e. with lower degree nodes since by construction the latter
are included in the periphery. Thus we have a large part of links (eventually a
majority) originating from core nodes which contribute negatively to degreedegree correlation. Moreover, since links between peripheral nodes (which
would increase the degree-degree correlation) are rare, most peripheral nodes
will link with core nodes, adding a further negative contribution to degreedegree correlation, in this case originating from peripheral nodes.
Following this argument we may distinguish between strong and weak
cores. The former are associated with disassortativeness, which implies that
core members have many connections with peripheral nodes, i.e. their average degree is largely in excess of core size. This feature is typical of highly
concentrated economic networks such as the interbank credit market, whose
core nodes are connected to virtually any other node in the network (Bargigli
et al., 2014). Nodes belonging to weak cores have instead an average degree
which is not too distant from the size of the core itself. In our case we see
from Tab. 3 that, for instance, there are 69 boards in the core in 1952. These
boards have on average a degree equal to 895 . Thus they simply cannot be
connected to a large part of peripheral nodes, which count in the thousands
in our networks. This conclusion is consistent with the overall assortative
5
We observe a similar behavior also in the other years.
13
behavior of the networks highlighted in the previous section. Thus we may
conclude that, loosely speaking, the cores of our networks are weak in all
years.
We characterize cores in terms of a set of node properties or characters,
following the approach of Tumminello et al. (2011). In this case we make the
null hypothesis that the frequency of each (discrete) character (e.g. belonging
to a given sector or being a SOE) in the core is expected not to be different
from the frequency of the same character in the overall sample. In other
terms, according to the null hypothesis, the frequency of node characters
in the core follows an hyper-geometric distribution. Thus we evaluate the
p-value of observed community frequencies according to this distribution,
while taking care of multiple hypothesis testing by means of the FDR (False
Discovery Rate) procedure at confidence level α = 1%.
The results are summarized in Tab. 3. For each year, we report the size
of the core in term of nodes, its share of total assets, which sectors and Stateowned business groups are significant characters in the sense specified above,
and eventually other significant characters6 . For each significant character,
in brackets we report the percentage frequency in the overall sample and in
the core. With the obvious exception of 1952, we also consider as a significant
character that of being part of the core in the previous benchmark year. In
this way we can test the temporal continuity of cores, which turns out to be
high, since core membership in each year is always significantly related to core
membership in the previous year. Cores are mostly composed of large listed
companies, with a significant participation of SOEs of the IRI group. The
sectoral characterization confirms the marginalization of electrical firms after
nationalization in 1962, which were already highlighted in previous works
(Bargigli and Vasta, 2006). On the other hand, banks turn out to have a role
in the core even before the nationalization of electrical companies.
6
Betweenness
The centrality of a node can be captured by the fact that this node acts
as an intermediary in the network. The betweenness of a node is equal to
the number of shortest paths between all couples of nodes that pass through
that node. A node with high betweenness centrality has a large influence on
6
The characters taken into consideration are being a large firm (i.e. belonging to upper
ten percentiles of the distribution of total assets in the year) and being a quoted company.
14
year
Nodes
TA(%)
Sector (%)*
SOE (%)*
1952
69
26
40(3;36),
66(2;9)
IRI(3;26)
1960
71
20
40(3;49),
65(8;28)
IRI(3;17)
1972
66
17
64(0;3),
65(7;33),
66(1;15)
IRI(2;23)
1983
40
6
65(23;55),
66(2;15)
IRI(6;20)
Other (%)*
QF(3;46),
LF(8;75)
QF(3;49),
LF(8;70),
52.core(3;66)
QF(1;41),
LF(8;68),
60.core(2;46)
QF(3;55),
LF(10;65),
72.core(4;47)
Legend: 40 = Electricity, 64 = Telecoms, 65 = Banks, 66 = Insurance, LF =
Large firm, QF = Quoted firm. * The first percentage in brackets refers to the
frequency among all nodes, the second percentage to the frequency in the core.
Table 3: Core characterization
the transfer of items through the network, under the assumption that items
follow the shortest paths.
Tables 4-7 below show the twenty most central companies according to
betweenness firms in the benchmark years. Only three firms are present in
all of them, one insurance company (RAS Riunione Adriatica di Sicurtà),
and two banks (Istituto centrale di Banche e Banchieri; Banca d’America e
d’Italia). More in general banks and insurance firms are central in all the
years considered. Banks prevail in all years with around 50% on average
of the 20 most central firms. Insurances come always as the second sector.
As to industrial sectors, electricity and gas companies appear before the
nationalization of electricity (1962), while the chemical sector occurs in 1983.
Regarding ownership, most of central firms are listed, private companies.
Among banks, both State and private financial holdings have an important
role. On the private side, for example, Strade Ferrate Meridionali in 1952, as
the most important private financial company which later changed its name
in Bastogi (that we find in 1971), and La Centrale in 1983, were often main
players in several Italian financial affairs. Bastogi, for example, was the first
“salotto buono” of Italian capitalism and managed the funds resulting from
the nationalization of electrical industry. It was also the first company to be
defended by Mediobanca, in name of the stability of the system, against an
IPO launched in 1971 by Michele Sindona. La Centrale was involved both
15
in Sindona and Roberto Calvi affairs. SO financial holdings are also present
with STET (Telecommunications) in 1960 and 1971 and Finsider (Steel) in
1960.
The continuity in the sectoral composition of central firms along the entire
period has its roots in the systemic view of the Italian economy established in
1933 and lasting without significant changes up to the end of the Seventies.
The Italian economy in the postwar period was managed under the State
control of financial flows, including the funding of State owned and private
industrial firms. In this context Bank of Italy played a crucial role inspired to
a “sui generis monetarism”. According to this view, the balance of payments
depended both on the relationship between money demand and supply, and
on domestic and external prices. This mechanism regulated also financial
flows to companies that had at the center the banking system which was,
since 1933, predominantly State owned. Commercial banks supplied shortterm funds and could not have a stake in industrial companies. Private
and SO financial companies instead provided medium and long term funds
to industrial firms, collecting resources within forms and limits set by the
Central Bank. In legal terms, the system was formed mainly by public bodies,
which were supervised by the Bank of Italy to avoid bankruptcy through
careful administrative inspections. Credit was supplied, in short, as a public
good by which the authorities were able to guide the strategies of enterprises,
mainly according to national industrial strategies.
This framework was strengthened by the role of SOEs which shared the
same goals and mentality. The planned nature of business financing was
further strengthened in 1967 when a branch of government, the CIPE (Interministerial Committee for Economic Planning) received the task of issuing
directives and to approve corporate plans, and in 1977 when the interministerial Committee for industrial Policy (CIPI) was established. This
institutional framework started to crush down at the end of Seventies, but
lasted up to the outbreak of the 1992 crisis and the following wave of liberalization and privatizations.
7
Communities
In this section we perform the community decomposition of the Italian boards
network. The general intuition behind the notion of community is that nodes
may eventually form subsets which are more densely interconnected among
16
Company
RAS RIUNIONE ADRIATICA DI SICURTÀ
TORINO ESPOSIZIONI
E.F.I. ENTE FINANZIAMENTI INDUSTRIALI
ISTITUTO CENTRALE DI BANCHE E BANCHIERI
SOCIETÀ ITALIANA PER LE STRADE FERRATE MERIDIONALI
BANCA PROVINCIALE DI DEPOSITI E SCONTI
CONIEL COMPAGNIA NAZIONALE IMPRESE ELETTRICHE
ITALGAS SOCIET ITALIANA PER IL GAS
BANCA COMMERCIALE ITALIANA
CARTIERE BURGO
VIZZOLA
CHATILLON
BANCA MILANESE DI CREDITO
COMPAGNIA DI ROMA RIASSICURAZIONI
DOMUS AMBROSIANA
BANCA POPOLARE DI NOVARA
BANCA D’AMERICA E D’ITALIA
BANCA PROVINCIALE LOMBARDA
L’ASSICURATRICE ITALIANA
SOCIETÀ SVILUPPO AGRICOLO MEZZOGIORNO SVAM
Characters
66, QF
74
65
65
65, QF, IRI
65
40, IRI
40, QF
65, IRI
21, QF
40, QF, IRI
24, QF
65
66
70
65
65
65
66, QF
73
Table 4: Top 20 firms by betweenness, 1952
Company
RAS RIUNIONE ADRIATICA DI SICURTÀ
ISTITUTO CENTRALE DI BANCHE E BANCHIERI
ITALGAS SOCIETÀ ITALIANA PER IL GAS
SOCIETÀ ITALIANA PER LE STRADE FERRATE MERIDIONALI
TORINO ESPOSIZIONI
BANCA PROVINCIALE LOMBARDA
CREDITO COMMERCIALE
L’ASSICURATRICE ITALIANA
CREDITO LOMBARDO
BANCA UNIONE
LINIFICIO E CANAPIFICIO NAZIONALE
CARBOLIO FORNITURE INDUSTRIALI DI OLII E COMBUSTIBILI
CARTIERE BURGO
ELETTRICA BRESCIANA
STET SOCIETÀ FINANZIARIA TELEFONICA
CHATILLON
ISTITUTO COMMERCIALE LANIERO ITALIANO
FINSIDER SOCIETÀ FINANZIARIA SIDERURGICA
BANCA D’AMERICA E D’ITALIA
STEI SOCIETÀ TERMOELETTRICA ITALIANA
Table 5: Top 20 firms by betweenness, 1960
17
Characters
66, QF
65
40, QF
65, QF
74
65
65
66, QF
66
65
17, QF
23
21, QF
40, QF
65, QF, IRI
24, QF
65
65, QF, IRI
65
40, ENI
Company
RAS RIUNIONE ADRIATICA DI SICURTÀ
ISTITUTO CENTRALE DI BANCHE E BANCHIERI
BASTOGI FINANZIARIA
L’ASSICURATRICE ITALIANA
STET SOCIETÀ FINANZIARIA TELEFONICA
UNIONE ITALIANA DI RIASSICURAZIONE
S.I.A.T. SOCIETÀ ITALIANA ASSICURAZIONI TRASPORTI
EFIBANCA ENTE FINANZIARIO INTERBANCARIO
BANCA D’AMERICA E D’ITALIA
BANCA CATTOLICA DEL VENETO
ITALGAS SOCIETÀ ITALIANA PER IL GAS
ISTUD ISTITUTO STUDI DIREZIONALI
TRAFORI DELLO SPLUGA E DELLO STELVIO
CREDITO VARESINO
MAGAZZINI STANDA
COTONIFICIO OLCESE VENEZIANO
SNIA VISCOSA
ING. C. OLIVETTI & C.
CREDITO COMMERCIALE
BANCA PROVINCIALE LOMBARDA
Characters
66, QF
65
65, QF
66, QF
QF, IRI
66
66
65
65
65
QF, ENI
73
45
65
52
17
24
30, QF
65
65
Table 6: Top 20 firms by betweenness, 1972
Company
ISTITUTO CENTRALE DI BANCHE E BANCHIERI
BANCA D’AMERICA E D’ITALIA
SNIA BPD
RAS RIUNIONE ADRIATICA DI SICURTÀ
I.M.I. ISTITUTO MOBILIARE ITALIANO ROMA
I.M. INTERMETRO
MIRA LANZA
LA CENTRALE FINANZIARIA GENERALE
SOCIETÀ ITALIANA ASSICURAZIONE CREDITI SIAC
UNIONE ITALIANA DI RIASSICURAZIONE
ING. C. OLIVETTI & C.
MONTEDISON
CONSORTIUM
LA RINASCENTE
COMPAGNIA FINANZIARIA LIGURE PIEMONTESE COFILP
COMPAGNIE INDUSTRIALI RIUNITE CIR
FINANZIARIA REGIONALE PIEMONTESE
CIGAHOTELS COMPAGNIA ITALIANA GRANDI ALBERGHI
BANCA CATTOLICA DEL VENETO
PIRELLI
Characters
65, IRI
65
24, QF
66, QF
65
45, EFIM, IRI
24, QF
65, QF
66, IRI
66, IRI
30, QF
24, QF
65
52, QF
65
65, QF
65
65, QF
65, QF
65, QF
Table 7: Top 20 firms by betweenness, 1983
18
themselves than with the rest of the network. In terms of matrix representation, community decomposition is equivalent to finding a permutation
matrix (i.e. a reordering of nodes) according to which the original adjacency
matrix is as similar as possible to a block diagonal matrix7 . This general intuition has found numerous applications in a wide range of fields, by means
of various specifications and tools 8 .
We adapt the original idea of defining communities with respect to a
statistical null hypothesis, proposed by Newman (2006), to the statistical
inference framework outlined in Sec. 4. The null hypothesis is given by the
“generalized” specification mentioned above (see also Appendix B). In this
random network model, connections depend only on degrees and strengths.
We define a community as a subset of nodes whose reciprocal connections are
significantly in excess of what we could expect to observe in a large sample
of these random networks. In particular, we employ z-scores in order to test
not only intramodular connections but also intermodular connections (see
appendix C). In the first case, if the null hypothesis is rejected we obtain a
statistical validation of the community partition. In the second case, under
the same condition, we obtain a set of statistically significant links between
different communities.
Similarly to hierarchical data clustering, community detection requires
that we choose a “preferred” level of description, which equates to determining the number of communities we would like to obtain. We follow a bottomup approach, according to which we determine the number of communities
and the decomposition at the same time. For all details see Appendix C.
In order to avoid the potential biases stemming from the very different
sample size in different years, we use restricted samples by selecting, for
each year, the largest 3, 763 companies by total assets, corresponding to the
size of the smallest sample (see Tab. 1). The algorithm is applied on the
largest component of each of these sub-networks, since the decomposition in
case of different components is trivial (in fact each component is a distinct
community).
Our algorithm turns out to be very effective: all communities are significant in terms of z-scores (z > 1.96), and a number of significant connections
between communities emerge (Fig. C.2 of Appendix C). In Fig. 3 we depict
7
For more details see appendix C, where we also explain the difference between community and core-periphery partitions.
8
For a general introduction to this field see Fortunato (2010).
19
only the latter. Their decline over time reflects the “rarefaction” of links
already observed for other national systems (see Sec. 4). Moreover, we see
that the number of communities is steadily decreasing, from 30 in 1952 to 18
in 1983. At the same time, the relative weight of the largest community in
terms of nodes is relatively stable around 10% in the first three benchmark
years, but doubles to 22% in 1983. The impression we get at first glance from
Fig. 3 is that of a system undergoing a transformation, whereby a network
formed by a larger number of smaller and variously connected communities
is replaced by a network whose nodes entertain fewer, but more focused,
relationships.
We characterize each community in terms of a set of node properties,
following the methodology described in sec. 5. The results of this analysis
are summarized in Tabs. 8-11. For each community, we report the size in
term of nodes, the share of total assets, which sectors and State-owned business groups are significant characters of the community in the sense specified
above, and eventually other significant characters9 . For each significant character, we report in brackets the percentage frequency in the overall sample
and in the given community. With the obvious exception of 1952, we also
consider as a significant character that of being part of a given community in
the previous benchmark year. In this way we can test the temporal continuity of communities, which turns out to be pretty low: only a small number
of communities is significantly related to some community in the previous
network observation. This result contrast with the relative stability of core
composition shown above. We also observe that in each year the core is significantly related to, although not perfectly overlapping with, one or more
communities.
The fraction of nodes belonging to non characterized communities (last
line of each table) decreases from 34% in 1952 to 23% in 1960, then increases
to 33% in 1972 and finally falls to zero in 1983. This result supports the
idea that relationships have become more focused, with sectoral variables
seemingly playing a major role. An interesting result regards the relative
frequency of diversified and not diversified communities, i.e. whether a community is characterized or not by a single sector or few strongly related sectors. The typical Big business organizational form of the Fifties and Sixties
9
The characters taken into consideration are being a large firm (i.e. belonging to upper
ten percentiles of the distribution of total assets in the year), being a quoted company and
being in the core of the network in the same year.
20
(a) 1952 (30 communities)
(b) 1960 (29 communities)
(c) 1972 (22 communities)
(d) 1983 (18 communities)
Figure 3: Communities and their significant relationships. Circles are proportional the size of communities. Edge thickness is proportional to z-scores.
21
was specialized, while the conglomerate form, which is typically diversified,
diffused in the Seventies. In our case the timing of the diffusion of big business in the Golden Age seems to correspond to the standard view: in 1952
diversification is low (10% of communities), increases a bit to 24% of total
communities in 1960, but decreases in 1972 when diversified communities
cover 9% of the total. This may be related to the industrial policy of the
time pursuing economies of scale according to the paradigm of Big Business. The 1983 benchmark shows instead a typical diversified feature (55%
of communities are diversified).
Historical evidence helps to explain these figures. The decline of profits
which characterized the Seventies and the need to reduce their debt burden
pushed firms to concentrate, regardless of industrial motivations. In 1975,
for example, the governor of Banca d’Italia Guido Carli proposed without
success to convert corporate debt obligations with the banking sector into
shares and in 1977 a law proposed by the finance minister Pandolfi reduced
the taxation of mergers, divisions, transfer of assets and exchange of shares
between companies. As we show below, there is a significant increase of concentration in our sample, which is consistent with this historical argument.
An original feature of the Italian system is the growing presence of SOEs.
The share of communities characterized by the presence of SOEs increases
from 13% in 1952 to double in 1960 (26%) and 1972 (23%) up to 44% in
1983. In this year the IRI financial holding belongs the large community 17,
which is connected, through community 10, to two communities (0 and 9)
in which we observe a very high frequency of IRI firms. Thus, SOEs retain
their central position up to the end of the period under consideration.
22
23
134
75
248
70
145
120
130
175
307
176
55
51
50
73
147
67
59
1,090
0
1
2
3
4
5**
8**
10
12**
17
18
19
20
21
23
25
27
Others
0.9
3.7
2.8
1.6
4.7
2.8
1.5
2.6
7.3
2.4
6.0
0.9
4.8
1.5
7.7
1.6
4.0
42.9
% Assets
15(7;15)
45(3;16)
17(10;23)
11(1;5),23(2;6)
20(1;5),51(8;17)
21(2;9),22(2;7),23(2;8)
17(10;30)
61(2;7),92(1;5)
40(4;14)
40(4;18)
17(10;29)
26(4;16)
26(4;25)
64(0;3)
60(3;13)
-
Sector (%)*
*
Iri(3;16)
Iri(3;15)
Iri(4;18)
Iri(3;15)
-
SOE (%)
LF(11;22)
LF(11;27)
LF(11;32)
LF(11;21)
52.core(2;13)
52.core(2;10)
-
Other (%)*
Table 8: Community characterization, 1952
Legend: 11 = Oil & Gas extr., 15 = Food & Bev., 17 = Textile, 20 = Wood, 21 = Paper, 22
= Publishing, 23 = Oil refin., 26 = Minerals, 34 = Cars, 40 = Electricity, 45 = Construction, 51
= Wholesales, 60 = Ground Transport, 61 = Water Transport, 64 = Telecoms, 92 = Movies &
Broadcast, LF = Large firm, QF = Quoted firm. * The first percentage in brackets refers to the
frequency among all nodes, the second percentage to the frequency in the community. ** We define
these communities as diversified on the basis of their characterizing sectors.
Size
id
24
48
62
126
82
114
76
358
79
112
222
31
228
103
237
93
13
157
35
241
705
0
1
2
3
4**
5**
6**
7**
9**
10**
11
12
14
17
20
22
24**
26
28
Others
22.8
18.7
0.4
4.8
1.6
6.3
2.7
1.2
2.3
1.1
8.8
14.4
1.0
1.7
1.2
0.7
0.6
3.2
4.1
1.8
% Assets
-
40(4;8)
30(0;15)
11(0;4),26(4;11)
62(0;6)
40(4;19)
13(0;3),20(1;5)
23(2;19)
17(9;14)
60(2;9),61(3;10)
26(4;11)
23(2;10),64(0;4)
40(4;15)
61(3;30)
15(8;19)
17(9;22),19(1;6)
15(8;17),93(0;5)
01(2;9),40(4;14)
17(9;13),65(10;15)
35(1;9),61(3;18)
Sector (%)*
-
Iri(4;12)
Eni(1;15)
Iri(4;20)
-
Iri(4;21)
Eni(1;6)
Iri(4;17)
-
SOE (%)*
-
-
-
-
5(2;9)
-
-
-
Comm.52 (%)*
LF(12;29),
52.core(3;18),
60.core(2;10)
QF(4;9),LF(12;24),
52.core(3;10),
60.core(2;8)
-
-
-
Other (%)*
Table 9: Community characterization, 1960
Legend: 01 = Farming, 11 = Oil & Gas extr., 13 = Metals mining, 15 = Food & Bev., 17 = Textile, 19 = Leather, 20 =
Wood, 23 = Oil refin., 26 = Minerals, 30 = Electr. machines, 35 = Other vehicles, 40 = Electricity, 45 = Construction,
60 = Ground trans., 61 = Water trans., 62 = Air trans., 64 = Telecoms, 65 = Banks, 85 = Health, 93 = Personal
services, LF = Large firm, QF = Quoted firm. * The first percentage in brackets refers to the frequency among all
nodes, the second percentage to the frequency in the community. ** We define these communities as diversified on the
basis of their characterizing sectors.
Size
id
25
1.7
5.6
21.0
18.3
9.7
12.1
10.9
6.5
1.6
8.1
1.6
0.8
1.5
% Assets
29(7;15)
65(10;25)
-
-
21(3;6)
15(7;18)
24(10;24)
26(6;13), 61(3;19)
23(2;15),
24(10;18),25(2;7)
23(2;15)
24(10;28)
17(7;15)
16(0;3),
32(1;8),64(0;3)
Sector (%)*
Iri(6;13)
Efim(1;15)
-
Iri(6;12)
Iri(6;16)
Iri(6;29)
-
Eni(2;19)
-
SOE (%)*
-
-
-
-
-
-
-
Comm.60 (%)*
-
-
1960.core(3;8)
1972.core(2;7)
-
LF(22;33)
-
-
-
Other (%)*
Table 10: Community characterization, 1972
11 = Oil & Gas extr., 15 = Food & Bev., 17 = Textile, 16 = Tobacco, 18 = Clothes, 21 = Paper, 23 = Oil refin., 24
= Chemicals, 25 = Plastics, 26 = Minerals, 29 = Mechanical equip., 32 = TV & radio, 34 = Cars, 61 = Water trans.,
62 = Air trans., 64 = Telecoms, 65 = Banks, 73 = R&S, LF = Large firm, QF = Quoted firm. * The first percentage
in brackets refers to the frequency among all nodes, the second percentage to the frequency in the community. **
We define these communities as diversified on the basis of their characterizing sectors.
139
59
915
16
20
Others
121
12**
251
202
152
177
8
11
13
15
188
6
271
100
70
154
2
3
5**
14
Size
id
26
54
61
67
62
151
136
121
124
154
179
143
144
123
278
144
147
79
600
0**
1**
2
3
4
5
6**
7
8**
9**
10
11**
12
13**
14
15**
16**
17**
25.3
9.2
1.5
3.1
5.0
19.2
0.8
3.2
1.0
4.2
3.3
0.9
0.4
0.6
2.9
1.3
15.9
2.0
% Assets
30(0;1),65(24;32),66(3;6)
35(2;9),73(0;2),64(0;2)
26(4;11),27(4;10)
61(1;7),24(5;12)
26(4;8),55(1;4),65(24;32)
02(0;3)
17(5;12),36(0;3)
17(5;13),45(4;16)
35(2;6),40(1;7),63(2;11)
27(4;10)
25(2;9),31(2;17),32(1;15)
13(0;3),18(1;8),26(4;15)
23(1;14)
36(1;4)
66(3;9)
17(5;14),24(5;23),70(4;10)
Sector (%)*
-
Efim(2;27)
Eni(3;10)
Iri(8;45)
Eni(3;19)
Iri(8;13)
Efim(2;7)
-
-
Iri(8;20)
Eni(3;11)
Eni(3;18)
SOE (%)*
-
-
-
8(4;13)
6(2;10),
11(2;9)
Comm.72 (%)*
LF(13;21)
LF(13;24)
QF(4;7)
1983.core(1;3)
-
1972.core(4;16)
-
Other (%)*
Table 11: Community characterization, 1983
Legend: 02 = Forests, 13 = Metals mining, 17 = Textile, 18 = Clothes, 23 = Oil refin., 24 = Chemicals, 25 = Plastics, 26 =
Minerals, 27 = Metallurgy, 30 = Electr. machines, 31 = Other machines, 32 = TV & radio, 35 = Other vehicles, 36 = Other
manufacturing, 40 = Electricity, 45 = Construction, 55 = Hotels 61 = Water trans., 63 = Other trans., 64 = Telecoms,
65 = Banks, 66 = Insurance, 70 = R. Estate, 73 = R&S, LF = Large firm, QF = Quoted firm. * The first percentage in
brackets refers to the frequency among all nodes, the second percentage to the frequency in the community. ** We define
these communities as diversified on the basis of their characterizing sectors.
Size
id
8
Panel analysis
In the previous sections we observed for the Italian board network a tendency
towards a “rarefaction” of connections which is shared by other national
systems. Now we try to investigate the factors behind this tendency. In
order to do so, we fit our dataset to the following fixed-effect panel model
kit = αi + βxt + γyit + ηAit + µbit + θzit + (1)
where kit is the log-degree of node i at t, xt and yit are vectors of time and
sectoral dummies, Ait and bit are respectively the logarithm of total assets
and of board size, zit is a vector of dummies codifying respectively if the
ith unit belongs to one of the three State-owned business groups (Iri, Eni,
Efim) and if it is quoted on the Italian stock exchange.
Our dataset consists of an unbalanced panel with t = 1, 2, 3, 4 and 10, 307
cross-sectional units10 . The choice of a RE model is rejected by the Hausman test. We resort to the unrestricted fixed-effect generalized least square
estimator (unrestricted FGLS) since in a first, restricted, estimation we observed a significant serial correlation of residuals. In the final estimation we
find that individual and sectoral effects are significant, and reject the hypothesis of cross-section correlation of residuals with Pesaran’s CD test11 .
Multicollinearity between regressors is also excluded.
From Tab. 12 we see that our model explains 90% of the observed variance. This result supports the idea that the degree of board networks is determined largely by economic variables. In particular, we see that both firm
and board size have a significant positive effect on k, while State-ownership
has either a non significant effect (Iri, Eni) or a significant positive effect
(Efim) with respect to private firms. Regarding time effects, we obtain a
strong confirmation of the “rarefaction” dynamics between 1972 (used as
reference) and 1983, as well as between 1952 and subsequent years.
In Tab. 13 we decompose the average log-difference of k into the contributions of the regressors. We see that the decrease between 1960 and 1972 can
be attributed to firm-specific effects, a decline in the average board size, and
a relatively small year effect. The decline between 1983 and 1972, instead,
is entirely determined by the latter, while all other regressors, and especially
firm and board size, together with firm-specific effects, contribute against it.
10
11
We perform the analysis in R (2008) with the plm package (Croissant and Millo, 2008)
For details and references see Croissant and Millo (2008).
27
Estimate Std. Error z-value
1952
0.1264619 0.0214194
5.9041
1960
0.0279453 0.0159786
1.7489
1983
-0.8001490 0.0243981 -32.7956
A
0.0805971 0.0080310 10.0357
b
0.7637770 0.0175933 43.4131
ENI
0.0035179 0.0787608
0.0447
EFI
0.7161572 0.0805559
8.8902
IRI
0.0645928 0.0474235
1.3620
LS
0.1147904 0.0445735
2.5753
--Signif. codes: 0 *** 0.001 ** 0.01 *
Total Sum of Squares: 24103
Residual Sum of Squares: 2320.3
Multiple R-squared: 0.90374
Pr(>|z|)
3.546e-09
0.0803053
< 2.2e-16
< 2.2e-16
< 2.2e-16
0.9643738
< 2.2e-16
0.1731851
0.0100151
***
.
***
***
***
***
*
0.05 . 0.1
Table 12: Unrestricted FGLS, dependent variable: log - degree (k). Sectoral
effects not displayed.
We also observe that State-ownership is never contributing significantly to
degree variations. In this sense, we find no evidence of a “marginalization”
of SOE as suggested by Rinaldi and Vasta (2012).
t
1960
t−1
1952
Firm eff.
0.005
Year eff.
-0.099
Sector eff. 0.017
A
0.097
b
0.001
SOE
0.000
List
-0.000
k̄t − k̄t−1
0.022
1972
1983
1960
1972
-0.136 0.227
-0.028 -0.800
0.008 0.044
0.035 0.235
-0.092 0.109
0.006 0.007
-0.002 0.002
-0.208 -0.176
Table 13: Decomposition of the average log-difference of k.
The interpretation of time effects is not easy. One possible explanation is
provided by non linear effects associated with firms’ size. If we compare for
instance the last two years, we see that a non linear relationship between k
and A is in place (Fig. 4, panel a). In fact, k is decreasing in the rightmost
28
part of the size distribution. In order to take this effect into account, we have
extended the model with a quadratic term in A. The resulting coefficient is
non significant, while the magnitude and significance of the others remain
stable (Tab. D.2, Appendix D). We have also estimated the model on a
subsample, from which we have excluded, for each year, firms belonging to
the largest 20 percentiles of the size distribution. We see that, although the
coefficient of the time effect for 1983 is larger, it still remains significant and
negative (Tab. D.3, Appendix D). These results are consistent with Fig. 4,
panel (b), which shows that the rarefaction occurs for all percentiles of the
size distribution, although it is stronger at the top.
180
60
1972
1983
160
50
140
120
1972
1983
40
k
k
100
80
60
30
20
40
10
20
0
106
107
108
A
109
1010
1011
0
0
log( )
(a) A versus k
20
40
q
60
80
100
(b) Average degree by percentile of size
Figure 4: Non linear relationship between size and degree: 1972 versus 1983.
Thus, although large firms have a greater role in the transformation of
the Italian boards network in the Seventies, we should seek for a factor affecting firms of all sizes in order to explain rarefaction. In particular, we can
envisage the following mechanism connecting decreasing degree with increasing concentration. A merger of two firms i and j, if their neighborhood is
overlapping, originates a new firm with combined size A0 > Ai + Aj and a
combined degree k 0 < ki + kj . As a consequence, the merger lowers the degree of neighbors which, thanks to assortativity, are mostly of the same size.
Thus we can make the hypothesis that an intense merger activity, especially
between large firms, may have contributed to the “rarefaction” of the overall
network, while being consistent with the nonlinear relationship between k
29
and A that we found. We have already mentioned in Sec. 6 the historical
evidence in favor of such a wave of mergers, especially in the late Seventies.
From Tab. 14 we see that our data provide robust evidence of an increasing
concentration of the corporate system. We leave for future research the task
of a rigorous statistical test of this hypothesis.
Herfindahl
Entropy
C4
1952
0.011 ± 0.000
−5.738 ± 0.002
0.112 ± 0.000
1960
0.010 ± 0.000
−5.883 ± 0.003
0.110 ± 0.000
1972
0.017 ± 0.000
−5.722 ± 0.004
0.162 ± 0.001
1983
0.025 ± 0.000
−5.219 ± 0.006
0.203 ± 0.001
Table 14: Concentration measures: average and 95% confidence intervals
(bootstrap estimation).
9
Conclusions
We describe how business networks among Italian firms evolve over the Long
Golden Age (1952-1983). We find some typical properties of these networks,
such as sparsity and connectedness in the same, large, giant component. Density is relatively low, while clustering and assortativity are relatively high and
stable over time. We compare the Italian network with other national networks as they emerge from the literature, showing that it is not different from
the latter when looking at the most relevant network properties. Since the
core of the network maintains a surprising stability over the time, particularly with respect to the centrality of its main actors, most notably banks and
insurances, we are able to revise some of the conclusions of previous works,
e.g. by showing that SOEs and banks maintain an important position in the
system throughout the period. SOEs in particular play a growing role in the
community structure of the network, while communities themselves become
more and more diversified by sector according to the conglomerate model of
the Seventies.
From an historical perspective, we integrate statistical network analysis
with two appreciative priors. The first one concerns the general consideration
of the Golden Age as the age of Big Business, while the second one concerns
the institutional environment of networks, especially the characters of a very
peculiar financial system such as the Italian one. Both could potentially affect
30
the structure of the network and its evolution over time. In particular, the
most relevant trend we observe is a decrease of connections which is common
to other national systems in the same period. We circumscribe the role of
cross-sectional variables, such as size or sectoral composition, and relate this
trend to an increasing concentration of the system in terms of assets. The
latter is an outcome of a wave of mergers, due to increasing indebtedness
of non financial companies with the banking system, which occurred in the
late Seventies. A rigorous statistical test of this hypothesis is left for future
research.
Acknowledgments: we thank Lorenzo Corsini and Pier Angelo Toninelli for their helpful
comments. All the usual disclaimers apply.
31
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A
Appendix: definitions
A graph or network G is given by the couple (V, E), where V is the vertex
or node set, typically assumed to be mapped onto a subset of N, while E is
the edge or link set, with E ⊂ N × N, and (i, j) = eij ∈ E can eventually
map onto a subset D of R or N. In this case w(eij ) = wij ∈ D is said to
be the strength of the link (i, j) and G is said to be weighted. In our case,
as explained below, the integer weights are given by the number of shared
directors between a couple of firms. If (i, j) maps onto 0, 1 we say that the
network is binary or unweighted. Further we set N = |V | and L = |E|.
We say that G is undirected or that it is a graph if we suppose eij ≡
eji for each i, j ∈ V . Otherwise we say that G is directed or that it is a
digraph. In general, G may be represented by the adjacency matrix A with
elements aij = 1 if eij ∈ E and aij = 0 otherwise. If G is undirected, A is
symmetric. The strength between nodes may be represented by the matrix
W with elements wij . From our definitions we see that a weighted network
can be always transformed onto a a binary network by setting aij = 1 if
wij > 0.
In order to obtain the boards network, we build in each year the matrix
representation of individual board membership. This is a N × M binary
matrix A, where N is the number of firms and M the number of individuals
in the dataset. Its entries aij are equal to one if the individual j is member
of the board of firm i. The matrix representation of the boards network
is obtained by self-multiplication W = AAT and we say that the boards
network is projected from the original bipartite network A. Each cell value
wij with i 6= j is equal to the number of shared directors between firms i and
j. Diagonal cells wii , which would take the board size of firm i as value after
multiplication, are set to zero in the undirected and weighted graph G which
is the subject of our analysis. The following definitions are tailored to this
case of interest.
The neighborhood of a node i is defined as the set ψ(i) of nodes such that
eij ∈ E for all j ∈ ψ(i). Then the degree ki is the cardinality of ψ(i). By
extension, the strength of a node may be defined as follows:
X
wi =
wij
(2)
j∈ψ(i)
The strength of a symmetric network is defined as w =
38
1
2
P
i
wi . The
degree (strength) assortativity coefficient ρk (ρw ) is defined as the Pearson
correlation coefficient of degrees (strengths) between pairs of linked nodes.
Then a network is said to be assortative if the degree (strength) of a node is
positively correlated with the degree (strength) of its neighbors. Otherwise,
it may be disassortative or uncorrelated.
An important set of network measures is related to connectivity. The
simplest example is given by density which for undirected graphs reads
D=
2L
n(n − 1)
(3)
Most social networks are found to display low density, i.e. to be sparse.
A network is said to be sparse when L N 2 . Otherwise the network is
said to be dense. A path in a graph is a sequence of vertices such that from
each of its vertices there is an edge to the next vertex in the sequence. Two
vertices i and j are said to be connected if G contains a path from i to j. A
connected component is a maximal connected subgraph of G. If most nodes
are connected by a path, we also say that G has a giant component Cg ,
meaning that most of the nodes lie on this single component. This property
is generally detected in social networks along with sparsity.
The distance or path length lij between two nodes i and j is defined as
length of the shortest (geodesic) path between i and j for i 6= j. If i and j
are not connected, we set lij = +∞. Then the average distance ¯l must be
computed separately for each of the connected components of G. In the text
we refer to the average distance in the largest component Cg .
In a binary undirected network the clustering coefficient cci of a node i
is defined as follows
P
h6=j aij ajh ahi
(4)
cci =
ki (ki − 1)
Instead the number of triangles T is defined as follows:
T =
1 XX
aij ajh ahi
3 i j6=h
39
(5)
B
Appendix: Null Network Models
In this appendix we summarize briefly the methodology of Park and Newman
(2004) . As stated in the main text (sec. 4), since network observables
depend on network realizations, their average are computed with respect to
the probability P (G) of observing a given realization G in an ensemble of
graphs G:
X
hxi i =
P (G)xi (G) = x̄i
(6)
G∈G
Since the xi (G) are a given, we need to specify a parameter dependent
functional shape of P (G) in order to solve the system (6). By adopting the
basic concepts of equilibrium statistical mechanics we obtain a solution for
this task by maximizing the following Lagrangean:
!
!
X
X
X
L=S+λ 1−
P (G) +
θi x̄i −
P (G)xi (G)
(7)
i
G
where S = −
obtain
P
G
G
P (G) ln P (G) is Gibbs entropy. By taking the f.o.c. we
ln P (G) + 1 + λ +
X
θi xi (G) = 0
(8)
i
Rearranging and taking antilogs:
e−H(G)
(9)
Z
P
where H(G) ≡
i θi xi (G) is the graph Hamiltonian which, thanks to
matrix representation of G, can be rewritten in terms of the matrix W or A,
and Z ≡ e(λ+1) is the partition
function. From the normalization constraint
P −H(G)
we easily obtain that Z = G e
. The model is solved when the values of
the parameters {θi }, which fully determine P , are obtained from the system
(6). It is possible to show that, if we adopt the Boltzmann-Gibbs distribution
(9), then the system (6) provides the maximum likelihood estimates for the
parameters {θi } (Garlaschelli and Loffredo, 2008).
P (G) =
40
B.1
Binary Configuration Model (Fermi networks)
When the constrained observables are the degree values {k1 , . . . , kn } of a
binary symmetric network, the main quantities of the model read:
H(G) =
XX
i
Z=
j>i
YY
i
[(θi + θj )aij ] =
XX
i
1 + e−Θij
Θij aij
j>i
j>i
F = − ln Z = −
XX
i
ln 1 + e−Θij
j>i
where Θij = θi + θj . The probability P (G) takes the form of the product
independent Bernoulli variables with parameters
of
n (n−1)
2
∂F
1
i = 1, . . . , n − 1
= Θij
(10)
j = i + 1, . . . , n
∂Θij
e +1
Substituting the last equation into the constraints we obtain the following
specialization of system (6):
pij = haij i =
1
= k̄i
+1
X
j6=i
eΘij
i = 1, . . . n
(11)
The system can be solved numerically in order to obtain the values θi
which satisfy the constraints.
B.2
Weighted Configuration Model
The Park & Newman maximum entropy model for weighted networks with
given average strength distribution leads to the following system:
X
j6=i
1
= w̄i
−1
eΘij
i = 1, . . . n
(12)
where w̄ = {w̄1 , . . . , w̄n } is the strength sequence we want our ensemble
to have in average. Unfortunately this ensemble, in which the wij are geometrically distributed, has one serious drawback. In fact, networks in this
ensemble are not bound to follow any topological property, such as the degree distribution or even connectivity. Regarding the latter, recent papers
41
(Mastromatteo et al., 2012; Musmeci et al., 2013) have highlighted that networks in this ensemble are, with high probability, dense while real networks
are mostly sparse (see appendix A). This property holds in particular for
the Italian board network we are analyzing (see sec. 4). In other words, the
topology of the weighted model is unrealistic by construction in most cases,
and in particular for networks in our dataset.
Bargigli (2014) develops an alternative weighted ensemble by introducing
the statistical weight W (G) of a network configuration and redefining the
Gibbs entropy as follows:
X
W (G) P (G) ln [W (G) P (G)]
(13)
S=−
G
The statistical weight counts the number of ways a given configuration
can be realized. The model of eq. (12) is derived for W (G) = 1, in analogy
with Bosonic systems of statistical physics. If we choose instead the Boltzmann weights W (G) = Πi Πj<i wij !, we conclude that the wij follow a Poisson
probability distribution and the system (6) becomes
X
λij = w̄i
i = 1, . . . n
(14)
j6=i
where λij = eΘij is the parameter of the Poisson distribution. Even if
the Poisson distribution just specified is much easier to estimate than the
system (12), it still leads to a unrealistic dense topology. In order to solve
rigorously this problem we would need to specify the network model with
a joint constraint on degrees and strengths (Bargigli, 2014).Unfortunately,
these models are in general rather difficult to solve numerically. For this
reason in our analysis we opt for a less rigorous approach, namely we suppose
that the weighted links of artificial networks in the ensemble are the product
of two independent variables:
wij ≡ aij bij ∼ Bernoulli(pij ) [Poisson(λij ) + 1]
(15)
1
obtained from (11). In order to obtain the parameters
+1
λij = xi xj we proceed to solve numerically the following specialization of
system (6):
with pij =
eΘij
42
X
X
X
hwij i =
haij bij i =
haij ihbij i =
j6=i
j6=i
j6=i
=
X
pij (λij + 1) =
j6=i
=
X
pij (xi xj + 1) = w̄i
i = 1, . . . n
(16)
j6=i
It’s easy to see that the ensemble defined in this way satisfies also the
constraints (11). In fact the topology is given by H (wij ) = aij . This is the
specification employed in sec. 4.
Finally, it is useful to compare our approach to the one proposed by
Conyon and Muldoon (2006).They compare real observations with an average computed for the configuration model of the original bipartite network
of boards and directors represented by the matrix A introduced in appendix
A. There are two fundamental departures from our approach. The first one
is that we simulate samples of random networks in order to derive the complete probability distribution of the different observables, whose knowledge is
necessary for statistical inference. By doing so, we need to rely on numerical
methods, since an analytical derivation of these probability distribution is
still lacking in the literature. The second one is that we specify our model
directly with respect to the projected network of boards (see appendix A).
We choose to proceed in this way because we want our model to follow the
original degree / strength distributions of the boards networks. Following
the approach of Newman et al. (2001), instead, Conyon and Muldoon (2006)
obtain boards networks with a degree distribution which is quite different
from the observed one.
C
Appendix: community detection
The community detection algorithm we employ in sec. 7 is a variant within
the well known set of spectral community detection methods, which rely on
the fact that eigenvectors represent the optimal solution for bisection problems if the latter are “relaxed” to the real field (Newman, 2006; Fortunato,
2010).While Donetti and Muñoz (2005)have already employed clustering algorithms to partition eigenvectors, their approach to community detection
43
relies on modularity optimization, which is affected by well known biases
(Fortunato, 2010).We leverage instead on the mutual orthogonality of eigenvectors in order to iteratively bisect nodes until a partition is finally obtained.
In practice, our algorithm works as follows:
1
1
1. perform the eigendecomposition of the matrix K = D− 2 W D− 2 , where
D is a diagonal matrix having on the main diagonal the strength sequence w = (w1 , w2 , . . . , wn ).
2. sort eigenvectors in decreasing order according to the magnitude of the
corresponding eigenvalue
3. set i = 2
4. bisect the ith eigenvector with a clustering algorithm12
5. if nodes in the smaller community are at least k = 5, assign them to
the ith community
6. delete the corresponding rows of the eigenvectors
7. if some nodes are not assigned to some community, set i = i + 1 and
to go to 4); else, end the procedure.
Once the partition is obtained, we perform a statistical validation of the
results using as statistical null hypothesis the “generalized” model detailed
by eqs. (15) and (16). The statistics we put to test are intramodular
and
P
P
intermodular connections. The former are defined as wHH = i∈H j∈H wij ,
j>i
where H stands for
set of nodes included in the hth community, while the
Pthe P
latter are wHQ = i∈H j∈Q wij for two distinct communities H and Q. This
j>i
approach is consistent with the multiscale view of communities, according to
which there are multiple optimal decompositions at different detail levels 13 .
12
In particular we employ the hierarchical clustering algorithm with cosine metric and
complete linkage.
13
Lambiotte et al. (2008) obtain these optimal decompositions by tuning a parameterdependent community detection algorithm. These decompositions may be related to the
time evolution of a diffusion or synchronization process taking place over the same network.
Loosely speaking, a finer decomposition corresponds to the early stages of these processes,
while a coarser one to later stages, at which the system is close to statistical equilibrium.
44
Thus two distinct communities which are significantly connected would be
considered as a single community at a coarser level of description.
The inference is performed with respect to z-scores. In order to compute
the latter we need to know the variance of wHH , wHQ . Since these are sum
of independent variables, their variance is the sum of the variance of the
summands which is obtained as follows:
σ 2 (wij ) =E(a2ij ) E(b2ij ) − E(aij )2 E(bij )2 =
=pij (1 + 3λij + λ2ij ) − p2ij (1 + λij )2 =
=pij λij + (1 − p2ij )(1 + λij )2
In order to highlight the relationship between core-periphery partitions,
community partitions and statistical validation, we depict the matrix representation of three different binary networks with the same number of nodes
and links. The first network (panel a) is strongly disassortative (ρk = −0.39)
and displays a strong core in the sense of Sec. 5. It is evident that there is no
way to obtain a block diagonal arrangement of this matrix, i.e. a community
decomposition of the network. The second network (panel b) is obtained
from the first by randomly reassigning links between core and periphery to
the periphery alone. It is strongly assortative (ρk = 0.39), with two communities.
While, as it appears, a strong core cannot coexist with a community
structure, this is not necessarily the case for a weak core. Indeed, the network
(b) has a weak core which, beyond sharing 18 members out of 21 with the core
of network (a), almost entirely overlaps with one of its communities. Finally,
in panel (c) we represent a realization of a Fermi network obtained using
the degree distribution of network (b). This network is weakly unassortative
(ρk = −0.05), with no clear community structure as we expected. Indeed the
value of intramodular connections observed in the first community of network
(b) (w11 = 532) is 1.78 times larger than the maximum corresponding value
observed in a sample of 100, 000 Fermi networks derived from the same degree
distribution. Thus we can conclude that the community decomposition of
network (b) is validated against the null model.
The results of z-score computations for our dataset are depicted in Fig.
C.2. Our algorithm turns out to be very effective: all communities are significant in terms of z-scores (z > 1.96), and a number of significant interconnections emerge among communities.
45
(a) Strong core network
(b) Two communities net-
(c) Fermi network
work
Figure C.1: Cores, communities and statistical validation
46
(a) 1952
(b) 1960
(c) 1972
(d) 1983
Figure C.2: Community detection results. The squares on the main diagonal
are proportional to the node size of communities. Colors correspond to zscores.
47
D
Supplementary Information
LLR
trunc. powerlaw
(p-value)
trunc. powerlaw
(p-value)
trunc. powerlaw
(p-value)
trunc. powerlaw
(p-value)
vs
vs
vs
vs
1952
powerlaw
0.0053
0.9183
lognormal
0.0143
0.8739
exponential
1.7598
0.2574
stretched exp. 51.937
0.0000
1960
1972
1983
0.1161 0.0114 6.8915
0.6299 0.8802 0.0002
0.0218 -3.5936 0.9196
0.4123 0.0258 0.0088
0.2333 0.6896 1.8434
0.6792 0.3513 0.2600
30.822 22.885 120.14
0.0000 0.0000 0.0000
(a) Degree
LLR
trunc. powerlaw
(p-value)
trunc. powerlaw
(p-value)
trunc. powerlaw
(p-value)
trunc. powerlaw
(p-value)
vs
vs
vs
vs
1952
powerlaw
10.941
0.0000
lognormal
0.9027
0.1173
exponential
1.0405
0.5095
stretched exp. 83.018
0.0000
1960
6.0232
0.0005
0.7012
0.0460
0.5624
0.5727
81.049
0.0000
1972
0.0544
0.7414
0.0160
0.5676
2.0122
0.3080
60.732
0.0000
1983
14.726
0.0000
0.9926
0.1074
1.0392
0.5111
132.35
0.0000
(b) Strength
Table D.1: Log-likelihood ratios of alternative degree / strength distributions
48
10
10
0
10
0
Data
Data
Tr. power-law fit
Tr. power-law fit
Power-law fit
Power-law fit
-1
10
10
-1
2
10
(a) 1952
10
2
(b) 1960
0
10
Data
0
Data
Tr. power-law fit
Tr. power-law fit
Power-law fit
Power-law fit
10
10
-1
-1
10
10
-2
2
(c) 1972
(d) 1983
Figure D.1: Degree distributions and fit
49
10
10
0
10
Data
Tr. power-law fit
Tr. power-law fit
Power-law fit
Power-law fit
-1
10
10
0
Data
-1
-2
10
10
-2
2
10
(a) 1952
10
2
(b) 1960
0
10
0
Data
Data
Tr. power-law fit
Tr. power-law fit
Power-law fit
10
Power-law fit
10
-1
10
10
-1
-2
-2
10
2
10
(c) 1972
(d) 1983
Figure D.2: Strength distributions and fit.
50
2
Coefficients
Estimate Std. Error z-value Pr(>|z|)
Y.f1952
0.12728935 0.02150346
5.9195 3.230e-09 ***
Y.f1960
0.02785868 0.01597402
1.7440 0.0811593 .
Y.f1983
-0.79656967 0.02549124 -31.2488 < 2.2e-16 ***
A
0.09347088 0.02953806
3.1644 0.0015539 **
A^2
-0.00041912 0.00092088 -0.4551 0.6490134
b
0.76346588 0.01759842 43.3826 < 2.2e-16 ***
EN
0.00645830 0.07900343
0.0817 0.9348478
EF
0.71708083 0.08054388
8.9030 < 2.2e-16 ***
IR
0.06538786 0.04744116
1.3783 0.1681126
L
0.11651034 0.04471587
2.6056 0.0091721 **
--Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1
1
Total Sum of Squares: 24103
Residual Sum of Squares: 2320.1
Multiple R-squared: 0.90374
Table D.2: Unrestricted FGLS with a quadratic term in A (see Sec. 8),
dependent variable: log - degree (k). Sectoral effects not displayed.
51
Coefficients
Estimate Std.Error z-value Pr(>|z|)
1952
0.251953
0.018193 13.8486 < 2.2e-16
1960
0.129061
0.013794
9.3566 < 2.2e-16
1983
-0.585260
0.028399 -20.6089 < 2.2e-16
A
0.113993
0.008647 13.1829 < 2.2e-16
b
0.498213
0.013330 37.3748 < 2.2e-16
ENI
-0.157236
0.172823 -0.9098 0.3629226
EFIM
0.834766
0.106331
7.8506 4.139e-15
IRI
0.169895
0.062409
2.7223 0.0064832
LS
0.160822
0.077232
2.0823 0.0373118
--Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1
Total Sum of Squares: 9795.8
Residual Sum of Squares: 1118.2
Multiple R-squared: 0.88585
***
***
***
***
***
***
**
*
1
Table D.3: Unrestricted FGLS for a subsample of smaller firms (see Sec. 8),
dependent variable: log - degree (k). Sectoral effects not displayed.
52